20.500.12556/RUL-173062 Evalvacija in primerjava sodobnih razpoznavalnikov govora Evaluation and Comparison of Modern Speech Recognizers V diplomski nalogi primerjam tri razširjene sisteme za avtomatsko razpoznavanje govora (angl. Speech-to-text (STT)) v slovenščini: Google Cloud Speech-to-Text, Microsoft Azure Speech Service in odprtokodni OpenAI Whisper. Za objektivno oceno sem oblikoval uravnotežen testni nabor govornih posnetkov iz korpusa Ar- tur 1.0, s poudarkom na prostih monoloških govorih (Artur-N) ter branih in studijskih posnetkih (Artur-B). Izbor vključuje približno eno uro govora 15–20 govorcev z raznolikimi demografskimi lastnostmi. Sisteme sem ocenjeval po na- tančnosti (angl. Word Error Rate – WER), časovni učinkovitosti (čas transkrip- cije) in praktičnih vidikih (enostavnost uporabe). Whisper sem poganjal lokalno na prenosnem računalniku s navadnim centralnim procesorjem (angl. CPU) z vnaprej prednaloženim modelom, storitvi Google in Azure sem uporabljal prek uradnih programskih vmesnikov (angl. API-jev). Celoten potek priprave govor- nih podatkov ter merjenja in izvoza preizkusnih rezultatov je bil avtomatiziran v programskih skriptah, kar omogoča ponovljivost in razširljivost pridobljenih ocen natančnosti teh sistemov. In this thesis, I compare three widely used speech-to-text (STT) systems for Slovene: Google Cloud Speech-to-Text, Microsoft Azure Speech Service, and the open-source OpenAI Whisper. For an objective assessment, I constructed a balanced test set of speech recordings from the Artur 1.0 corpus, with an emphasis on spontaneous monologic speech (Artur-N) as well as read and studio recordings (Artur-B). The selection comprises approximately one hour of speech from 15–20 speakers with diverse demographic characteristics. I evaluated the systems in terms of accuracy (word error rate — WER), time efficiency (transcription time), and practical aspects (ease of use). I ran Whisper locally on a laptop CPU with a preloaded model, while the Google and Azure services were accessed via their official application programming interfaces (APIs). The entire pipeline for preparing the speech data and for measuring and exporting the evaluation results was automated via scripts, enabling reproducibility and scalability of the evaluation process and results. razpoznavanje govora slovenščina Google Cloud Speech-toText Microsoft Azure Speech OpenAI Whisper WER Levenshtein Artur 1.0 primerjalna analiza speech recognition Slovenian Google Cloud Speech-to-Text Microsoft Azure Speech OpenAI Whisper WER Artur 1.0 benchmarking true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2025-09-12 11:30:25 2025-09-12 11:30:32 2026-02-09 13:50:52 0000-00-00 00:00:00 2025 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 62533 267817219 Nemanic_Valentin_-_Evalvacija_in_primerjava_sodobnih_razpoznavalnikov_govora.pdf Nemanic_Valentin_-_Evalvacija_in_primerjava_sodobnih_razpoznavalnikov_govora.pdf 1 477193F4DF68813E4F5CDD468E7EDB1F fcaf92624da4fb38acb238ffc22a982f4e6d469a1ec29cea27604b5350966596 f6acd93f-8fba-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=217229 Fakulteta za elektrotehniko 0 0 0